临床研究信息查询系统 ClinAgent 发布，基于 ReAct 模式提升效率
ClinAgent: A ReAct-Based Agent for Conversational Access to Clinical Trial Information
朋友，有个新工具叫 ClinAgent，专门帮做临床研究的，用起来很方便，比手动查资料快多了。
ClinAgent 是一个基于 ReAct 模式的对话式系统，用于查询临床试验信息。它通过整合 ClinicalTrials.
gov 搜索、PubMed 和本地结构化数据集，帮助研究人员用自然语言进行查询。研究比较了 Gemini 3.
0 Flash 和两种 DeepSeek V3.2 变体，结果显示 Gemini 在整体性能上表现最佳。
ClinAgent: A ReAct-Based Agent for Conversational Access to Clinical Trial Information
Querying clinical trial registries remains a manual and error-prone process, requiring researchers to navigate large volumes of semi-structured data without support for natural language interaction or cross-source synthesis. To address this, we introduce ClinAgent, a conversational system based on agentic Retrieval-Augmented Generation (RAG) that enables clinicians and researchers to query clinical trial information in plain language and receive grounded, up-to-date responses across multi-turn interactions. The system centers on a Large Language Model (LLM) agent following the ReAct paradigm, which iteratively reasons over queries, selects among a set of integrated tools, and refines its actions based on intermediate outputs. These tools include a ClinicalTrials.gov search interface, a PubMed module, and a Python-based analyzer operating on a locally cached structured dataset of clinical trials. We evaluate the system using a three-phase framework assessing operational effectiveness, planning quality, tool-use efficiency, and expert qualitative judgments, comparing three LLM backends: Gemini 3.0 Flash and two variants of DeepSeek V3.2 (thinking and non-thinking). Results reveal complementary strengths, with DeepSeek (thinking mode) excelling in planning quality, while Gemini achieves the highest overall performance and strongest expert ratings. Overall, our findings highlight the potential of agentic AI systems to improve the accessibility and synthesis of clinical trial information, supporting more efficient and user-centered biomedical research workflows.
本条情报归属 AITOP「论文」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」「中美大模型技术代差与商业化对照」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：ClinAgent、ReAct、临床研究、RAG，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始信息发布自一手权威信源「arXiv: DeepSeek」（Tier-1 一手发布），具备高确定性参考价值。
